Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

An Adaptability Analysis of the Space-Vehicle Traffic State Estimation Model for Sparsely Distributed Observation Environment

The autonomous driving has shown its enormous potential to become the new generation of transportation in the last decade. Based on the automated technology, vehicles can drive in a new form, vehicle platoon, which can significantly increase the efficiency of the road system and save road resources. The space-vehicle traffic state estimation model has shown its benefits in modeling autonomous vehicle platoon in nonpipeline corridors with on- and off-ramps in ideal observation environment. However, in the current initial stage of automated connected vehicles’ application, the observation environment is quite imperfect. Limited by financial and investment, traffic flow observation equipment is sparsely distributed on the road. How to adapt to the sparse observer layout is a critical issue in the current application of the space-time traffic state estimation, which is originally designed for the autonomous transportation. Therefore, this manuscript overviews the observation environment in practice and summarizes three key observation problems. This article designs 22 numerical experiments focusing on the three key issues and implements the space-time estimation model in different observation scenarios. Finally, the observation environment adaptability is analyzed in detail based on the experiment results. It is found that the accuracy of the estimation results can be improved with the highest efficiency under the premise of limited equipment input by reducing the observation interval to 1000 m and increasing the density of the observer to 1/km. For the road sections with relatively homogeneous traffic conditions, the layout of observation equipment can be relatively reduced to save the investment input. Also, the maintenance of observation equipment for the ramp with larger flow can be slowed down appropriately in limited equipment investment. This manuscript is of great practical significance to the popularization and application of connected automatic transportation.